| Summary: | 碩士 === 國立臺北科技大學 === 商業自動化與管理研究所 === 98 === Stock market is complicated and sensitive, which could be easily influenced by many factors. Due to there are nonlinear relationships between these factors and exist noise, using raw data to make predictions often cannot obtain good performance. So, we employ feature extraction methods to transform stock price data into feature space to reduce the drawbacks which above-mentioned and to rise the effectiveness of prediction outputs. In this research, nonlinear independent component analysis (NLICA) is applied as feature extraction method to time series data in order to discover hidden information, and particle swarm optimization (PSO) is applied as parameter-optimizing tool of support vector regression (SVR) to build up NLICA-PSO-SVR integrated time series forecasting model. The proposed model is compared with models which are integrated with other feature extraction methods. Due to NLICA can extract independent components (ICs) which are representing the main trend of stock price from observed data effectively, the performance of proposed model is better than other integrated models. On the other hand, integrating PSO to forecasting model can reduce computing cost and time-wasting dramatically and is also beneficial to forecasting performance.
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